1. Technical Field
The present disclosure relates to computer aided diagnosis and, more specifically, to automatic calibration of computer aided diagnosis based on retrospective examination.
2. Discussion of Related Art
Computer aided diagnosis (CAD) is the process of using computer vision systems to analyze medical image data and make a determination as to what regions of the image data are potentially problematic. Some CAD techniques then present these regions of suspicion to a medical professional such as a radiologist for manual review, while other CAD techniques make a preliminary determination as to the nature of the region of suspicion. For example, some CAD techniques may characterize each region of suspicion as a lesion or a non-lesion. The final results of the CAD system may then be used by the medical professional to aid in rendering a final diagnosis.
Because CAD techniques may identify lesions that may have been overlooked by a medical professional working without the aid of a CAD system, and because CAD systems can quickly direct the focus of a medical professional to the regions most likely to be of diagnostic interest, CAD systems may be highly effective in increasing the accuracy of a diagnosis and decreasing the time needed to render diagnosis. Accordingly, scarce medical resources may be used to benefit a greater number of patients with high efficiency and accuracy.
CAD techniques have been applied to the field of mammography, where low-dose x-rays are used to image a patient's breast to diagnose suspicious breast lesions. However, because mammography relies on x-ray imaging, mammography may expose a patient to potentially harmful ionizing radiation. As many patients are instructed to undergo mammography on a regular basis, the administered ionizing radiation may, over time, pose a risk to the patient. Moreover, it may be difficult to use x-rays to differentiate between different forms of masses that may be present in the patient's breast. For example, it may be difficult to distinguish between calcifications and malignant lesions.
Magnetic resonance imaging (MRI) is a medical imaging technique that uses a powerful magnetic field to image the internal structure and certain functionality of the human body. MRI is particularly suited for imaging soft tissue structures and is thus highly useful in the field of oncology for the detection of lesions.
In dynamic contrast enhanced MRI (DCE-MRI), many additional details pertaining to bodily soft tissue may be observed. These details may be used to further aid in diagnosis and treatment of detected lesions.
DCE-MRI may be performed by acquiring a sequence of MR images that span a time before magnetic contrast agents are introduced into the patient's body and a time after the magnetic contrast agents are introduced. For example, a first MR image may be acquired prior to the introduction of the magnetic contrast agents, and subsequent MR images may be taken at a rate of one image per minute for a desired length of time. By imaging the body in this way, a set of images may be acquired that illustrate how the magnetic contrast agent is absorbed and washed out from various portions of the patient's body. This absorption and washout information may be used to characterize various internal structures within the body and may provide additional diagnostic information.
Regardless of whether CAD is performed based on mammography or DCE-MRI, one or more regions of suspicion may be identified by the CAD system. Conventionally, a trained medical professional such as a radiologist may manually identify and classify the regions of suspicion within the medical image data. The radiologist may then manually characterize each region of suspicion in accordance with some quantitative guidelines. For example, regions of suspicion identified within the breast may be characterized according to Breast Imaging Reporting and Data Systems (BIRADS) guidelines.
The classification assigned to each region of suspicion may dictate the future course of action. For example, if the region of suspicion is classified as likely malignant, a biopsy may be ordered. If the region of suspicion is classified as normal, no further action may be taken. If, however, the region of suspicion is classified as a potential risk, the course of action may be to repeat the test in six months.
CAD systems have been developed to automatically classify a region of suspicion. These automatic systems may occasionally lead to a biopsy of a region of suspicion that turns out to be benign or may occasionally lead to a subsequent six-month evaluation that leads to a biopsy that establishes malignancy. Such findings may be suboptimal, as there is no need to biopsy a benign lesion and it is preferable not to wait six months before biopsying a malignant lesion.
The CAD system is generally programmed to minimize these sorts of over inclusion and under inclusion errors. Programming of the CAD system may utilize computer-learning approaches where training data is provided to help the CAD system learn the difference between a benign and a malignant lesion. However, once the CAD system has been fully programmed, additional program refinement is generally not performed. Accordingly, there is presently no way to refine CAD systems for the particular needs of the institution or physician that uses the CAD system.
Additionally, conventional CAD systems generally base their determination entirely on the image data collected. It must then be left to the medical professional to analyze this information in light of other pertinent information such as patient medical history.